Photometric image processing

ABSTRACT

An example method of photometric image processing may comprise: receiving a plurality of images of a three-dimensional object, wherein the plurality of images has been acquired by a plurality of cameras using a plurality of illumination and polarization patterns; performing color calibration of the plurality of images to produce a plurality of color-calibrated images; generating, using the plurality of color-calibrated images, a polygonal mesh simulating geometry of the three-dimensional object; producing a plurality of partial UV maps by projecting the plurality of color-calibrated images onto the polygonal mesh; generating a plurality of masks, wherein each mask of the plurality of masks is associated with a camera of the plurality of cameras, wherein the mask defines a UV space region that is covered by a field of view of the camera; blending, using the plurality of masks, the plurality of partial UV maps; and generating one or more texture maps representing the three-dimensional object.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/036,422 filed on Jul. 16, 2018, the entire content of which isincorporated by reference herein.

TECHNICAL FIELD

The present disclosure is generally related to image processing, and ismore specifically related to photometric image processing for producingtexture maps.

BACKGROUND

In computer-generated visual content (such as interactive video games),human bodies may be represented by various computer-generated objects,including polygonal meshes and texture maps. A polygonal mesh hereinshall refer to a collection of vertices, edges, and faces that definethe shape and/or boundaries of a three-dimensional object. A texture mapherein shall refer to a projection of an image onto a correspondingpolygonal mesh.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

The present disclosure is illustrated by way of examples, and not by wayof limitation, and may be more fully understood with references to thefollowing detailed description when considered in connection with thefigures, in which:

FIGS. 1A-1C schematically illustrate an example lighting assemblyimplemented in accordance with one or more aspects of the presentdisclosure;

FIG. 2 depicts a flowchart of an example photometric image processingworkflow, in accordance with one or more aspects of the presentdisclosure;

FIG. 4 shows an example image acquired using L_(c) illumination andpolarization pattern implemented in accordance with one or more aspectsof the present disclosure;

FIG. 4 shows an example image acquired using L_(p) illumination andpolarization pattern implemented in accordance with one or more aspectsof the present disclosure;

FIG. 5 shows an example image acquired using L_(x) illumination andpolarization pattern implemented in accordance with one or more aspectsof the present disclosure;

FIG. 6 shows an example image acquired using L{circumflex over ( )}_(x)illumination and polarization pattern implemented in accordance with oneor more aspects of the present disclosure;

FIG. 7 shows an example image acquired using L_(y) illumination andpolarization pattern implemented in accordance with one or more aspectsof the present disclosure;

FIG. 8 shows an example image acquired using L{circumflex over ( )}_(y)illumination and polarization pattern implemented in accordance with oneor more aspects of the present disclosure;

FIG. 9 shows an example image acquired using L_(z) illumination andpolarization pattern implemented in accordance with one or more aspectsof the present disclosure;

FIG. 10 shows an example image acquired using L{circumflex over ( )}_(z)illumination and polarization pattern implemented in accordance with oneor more aspects of the present disclosure;

FIG. 11 shows an example projected image partially covering the UV map,in accordance with one or more aspects of the present disclosure;

FIG. 12 shows an example albedo map produced by the example imageprocessing workflow implemented in accordance with one or more aspectsof the present disclosure;

FIG. 13 shows an example blended images that have been acquired usingL_(p) illumination and polarization pattern implemented in accordancewith one or more aspects of the present disclosure;

FIG. 14 shows an example blended images that have been acquired usingL_(x) illumination and polarization pattern implemented in accordancewith one or more aspects of the present disclosure;

FIG. 15 shows an example blended images that have been acquired usingL{circumflex over ( )}_(x) illumination and polarization patternimplemented in accordance with one or more aspects of the presentdisclosure;

FIG. 16 shows an example blended images that have been acquired usingL_(y) illumination and polarization pattern implemented in accordancewith one or more aspects of the present disclosure;

FIG. 17 shows an example blended images that have been acquired usingL{circumflex over ( )}_(y) illumination and polarization patternimplemented in accordance with one or more aspects of the presentdisclosure;

FIG. 18 shows an example blended images that have been acquired usingL_(z) illumination and polarization pattern implemented in accordancewith one or more aspects of the present disclosure;

FIG. 19 shows an example blended images that have been acquired usingL_(z) illumination and polarization pattern implemented in accordancewith one or more aspects of the present disclosure;

FIG. 20 shows an example reflectance map produced by the example imageprocessing workflow implemented in accordance with one or more aspectsof the present disclosure;

FIG. 21 shows an example photometric normal map produced by the exampleimage processing workflow implemented in accordance with one or moreaspects of the present disclosure;

FIG. 22 schematically illustrates an example image capture sequenceimplemented in accordance with one or more aspects of the presentdisclosure;

FIG. 23 depicts a flowchart of an example method for color calibrationimplemented in accordance with one or more aspects of the presentdisclosure;

FIG. 24 depicts an example color chart utilized by systems and methodsof the present disclosure;

FIG. 25 schematically illustrates producing depth and position mapsusing the ray tracing technique, in accordance with one or more aspectsof the present disclosure;

FIG. 26 schematically illustrates identifying, for each camera, UV mapregions with high distortion (e.g., distortion level exceeding apre-defined threshold), in accordance with one or more aspects of thepresent disclosure;

FIG. 27 shows an example mask defines a region of the UV map that isbest covered by the field of view a corresponding camera, in accordancewith one or more aspects of the present disclosure;

FIG. 28 shows example UV masks before and after performing the fusionblend operation, in accordance with one or more aspects of the presentdisclosure; and

FIG. 29 depicts a block diagram of an illustrative computing deviceoperating in accordance with one or more aspects of the presentdisclosure.

DETAILED DESCRIPTION

Described herein are lighting assemblies and image processing workflowsfor acquiring series of images of a three-dimensional object (e.g., amodel's head), processing the acquired images, and generating varioustexture maps. Such methods and systems may be employed, for example, ininteractive video game applications for generating visual objectsrepresenting game characters having visual resemblance with certainpersons (e.g., models or actors).

An example lighting assembly implemented in accordance with one or moreaspects of the present disclosure may include a mounting frame carryingmultiple lighting fixtures and still image cameras. Each lightingfixture may include a light source, a reflector, and one or morepolarization filters. The light intensity of each individual lightsource and/or the polarization angles of each individual polarizationfilter may be adjusted in order to produce various light andpolarization patterns. In an illustrative example, the lighting assemblymay be equipped with two sets of lights, such that the first set oflights is equipped with horizontal polarization filters and the secondset of lights is equipped with vertical polarization filters, which maybe controlled by a programmable controller. The programmable controllermay further synchronize triggering the camera shutters with light andpolarization patterns produced by the light sources, as described inmore detail herein below with references to FIG. 1.

The cameras and light sources of the lighting assembly may undergo acalibration process, which ensures that the cameras are aimed toconverge at the center of the cylindrical space and focus on a sphere ofa pre-defined diameter or a manikin head which is placed at the centerof the cylindrical space. The light sources are aimed to the center ofthe cylindrical space and each light is calibrated to an intensity valuethat would match an expected illuminance value read from a light meterplaced on the surface of a sphere pointed directly at the light. Thesecalibrated values may be used as a base input into the lighting patternsin order to ensure even lighting as multiple lights are switched on andconverge on the objects surface.

Upon acquiring the images, each camera may upload the acquired imagesvia a network connection to a server for further processing andgenerating the texture maps. In an illustrative example, the photometricimage processing workflow starts by calibrating the acquired imagesusing a reference color chart. The calibrated images are thenun-distorted based on the lens and camera sensor configurations. Theundistorted images are projected onto a reconstructed polygonal meshrepresenting the imaged object and mapped into the UV space. UV spaceherein refers to a two-dimensional space produced by projecting atwo-dimensional image onto a three-dimensional object, where the lettersU and V denote the axes of such space.

In order to produce a single UV map, partial UV maps produced byprojecting the images acquired by different cameras are blended by themask generation and fusion blend operations. The mask generationproduces, for each camera and illumination pattern, a mask that definesa region of the UV map that is best covered by the field of view of thatcamera. The masked images are then blended by the fusion blendoperation, which creates seamless maps without losing the details andblurring the image. The blended images are utilized for generatingreflectance, photometric normal, displacement maps, and albedo map, asdescribed in more detail herein below.

The polygonal meshes and texture maps produced by the image processingworkflow may be utilized by various applications, such as interactivevideo games. In an illustrative example, one or more files containingthe polygonal meshes and textures may be distributed to one or moreclient gaming devices on the computer-readable media carrying executableinteractive video game files. In another illustrative example, one ormore files containing the polygonal meshes and textures may bedownloaded and displayed by one or more client gaming devices over anetwork from a gaming server. Various aspects of the above referencedmethods and systems are described in more detail herein below by way ofexamples, rather than by way of limitation.

FIGS. 1A-1C schematically illustrates an example lighting assemblyimplemented in accordance with one or more aspects of the presentdisclosure. FIG. 1A shows the side view, FIG. 1B shows the top view, andFIG. 1C shows the inside surface view. The example lighting assembly 100may include a mounting frame 110 carrying multiple lighting fixtures 115and camera mounts 120 for mounting still image cameras.

The mounting frame 110 may comprise a plurality of vertical bars 125which may be attached to each other by horizontal joists 130, 135 insuch a manner that the vertical bars would lie on an imaginarycylindrical surface. In an illustrative example, the cylindrical surfacemay cover a cylindrical segment of approximately 210 degrees. In otherwords, the projection of the cylindrical surface on a horizontal planewould produce an arc of approximately 210 degrees. In variousimplementations, this angle may vary between 180 and 270 degrees.

The height of the mounting frame may be calculated in such a manner thatthe head of the model 140 positioned within the lighting assembly wouldbe near the center of the vertical axis of symmetry of the lightingassembly. In certain implementations, the model may be positioned on achair with an adjustable height.

The vertical bars and horizontal joists may appear in various shapes andprofiles, e.g., T-beams, H-beams, tubular beams, etc. The vertical barsand horizontal joists may be made of a variety of materials, includingmetal, wood, plastic, or any combinations of these and/or othermaterials.

In an illustrative example, lower ends of the vertical bars may beattached to the lower horizontal joist 130 having an arc shape; thevertical bars may be further attached to each other by a plurality ofupper horizontal joists 135, such that each upper horizontal joist mayhave a shape of an arc and may be located in the spatial proximity ofthe upper end of the vertical bars.

The mounting frame may carry a plurality of lighting fixtures 115 whichmay be attached to the mounting frame in a grid fashion, as shown inFIG. 1C. Each lighting fixture 115 may include a light source (e.g., alight emitting diode (LED) panel, a discharge lamp, a halogen lamp,etc.), a reflector, and one or more polarization filters. A lightingfixture 115 may be attached to a vertical bar and/or a horizontal joistby an adjustable fastening mechanism which allows changing the directionof the light beam emitted by the lighting fixture. In an illustrativeexample, two or more lighting fixtures 115 may be oriented in such amanner that their respective light beams converge in a specified pointlocated in a spatial proximity of the axis of symmetry of the lightingassembly. In certain implementations, the fastening mechanisms may becontrolled by a programmable controller.

The light intensity of each individual light source may be adjusted,e.g., by a programmable controller which may communicate to the lightingfixtures using DMX protocol. In an illustrative example, multiple lightsources which are mounted at the same height measured from the lower endof the lighting assembly may be configured to produce the same lightintensity. Furthermore, light intensity of a light source locatedfurther away from the center of the vertical axis of symmetry of thelighting assembly may be higher than the light intensity of anotherlight source located closer to the center point of the vertical axis ofsymmetry, so that both light sources would produce the same scenebrightness.

As noted herein above, each lighting fixture 115 may further include apolarization filter. The polarization angle of the filter may beadjustable, in order to produce various polarization patterns, which aredescribed in more detail herein below.

The mounting frame 110 may further carry a plurality of still imagecameras, which may be attached to the mounting frame by camera mounts120. The cameras may be positioned and calibrated in order to producehigh-quality images of a model which is positioned approximately in thecenter of the vertical axis of symmetry of the lighting assembly. In anillustrative example, the cameras may be positioned in a grid fashionbetween the lighting fixtures 115, as schematically illustrated by FIG.1C.

Each camera may be equipped with a wireless triggering device, such thatmultiple cameras may be controlled by a programmable controller. Theprogrammable controller may synchronize triggering the camera shutterswith setting light and polarization patterns produced by the lightsources. Various light and polarization patterns are described in moredetail herein below. In an illustrative example, the controller may pollall cameras and upon receiving an acknowledgement from all requisitecameras, adjust the polarization filters and initiate a lightingsequence to be produced by lighting fixtures. Thus, for each facialexpression, a series of images with different lighting and polarizationpatterns may be acquired by each camera. Upon acquiring the images, eachcamera may upload the acquired images via a network connection to aserver for further processing, e.g., as described in more detail hereinbelow with reference to FIG. 2.

In certain implementations, the lighting assembly may be furtherequipped with a reference video system comprising one or more videocameras and one or more video screens configuring to display the videofeeds received from the video cameras in order to provide a visualfeedback to the model positioned within the lighting assembly.

FIG. 2 depicts a flowchart of an example photometric image processingworkflow 200 which may be employed for acquiring and processing imagesof a three-dimensional object (e.g., a model's head), in accordance withone or more aspects of the present disclosure. The example imageprocessing workflow 200 and/or each of its individual functions,routines, subroutines, or operations may be performed by one or moreprocessors of a computer system (e.g., the computer system 2900 of FIG.29), and may further employ one or more cameras, lighting assemblies,light synchronization controllers, and/or other equipment. The imageprocessing workflow 200 may be performed by one or more processingthreads, each thread executing one or more individual functions,routines, subroutines, or operations of the method.

As schematically illustrated by FIG. 2, the processing workflow 200 maystart, at block 210, by acquiring one or more series of images of amodel. In an illustrative example, a series of images of a model may beacquired using the lighting assembly 100 of FIG. 1. For each facialexpression, a series of images with different lighting and polarizationpatterns may be acquired by each camera.

In an illustrative example, the first illumination and polarizationpattern (denoted as L_(c)) may provide full-on illumination withcross-polarized filters, such that a polarization filter is installedvertically at each camera lens and another polarization filter isinstalled horizontally in front of each light source. Using thisillumination and polarization pattern allows capturing diffuse lightsonly, and hence, the images produced using this pattern may be utilizedfor creating albedo and reflectance maps. FIG. 3 shows an example imageacquired using L_(c) illumination and polarization pattern.

The second illumination and polarization pattern (denoted as L_(p)) mayprovide full-on illumination with parallel-polarized filters, such thatall polarization filters are vertically positioned. Using thisillumination and polarization pattern allows capturing specularreflection and diffuse lights, and hence, the images produced using thispattern may be utilized for geometry reconstruction (i.e., generating apolygonal mesh representing the geometry of the imaged object) andgenerating the reflectance map. FIG. 4 shows an example image acquiredusing L_(p) illumination and polarization pattern.

Six more illumination and polarization patterns (denoted L_(x), L_(y),L_(z), L{circumflex over ( )}_(x), L{circumflex over ( )}_(y), andL{circumflex over ( )}_(z)) employ parallel-polarized filters andpartial illumination to generate the photometric normal map. FIG. 5shows an example image acquired using L_(x) illumination andpolarization pattern. FIG. 6 shows an example image acquired usingL{circumflex over ( )}_(x) illumination and polarization pattern. FIG. 7shows an example image acquired using L_(y) illumination andpolarization pattern. FIG. 8 shows an example image acquired usingL{circumflex over ( )}_(y) illumination and polarization pattern. FIG. 9shows an example image acquired using L_(z) illumination andpolarization pattern. FIG. 20 shows an example image acquired usingL{circumflex over ( )}_(z) illumination and polarization pattern.

In various alternative implementations, other equipment and/orillumination and polarization patterns may be employed for acquiringseries of images of the model.

The acquired images are calibrated using a reference color chart (block215), as described in more detail herein below. After calibration,marker detection (block 220) may be performed on the images which havebeen acquired with L_(p) illumination and polarization pattern (full-onillumination with parallel polarized filters) in order to detectpositions of markers of a pre-defined color (e.g., green) in each image.The marker positions are then utilized for reconstructing the geometryof the three-dimensional object (block 225) and generating athree-dimensional polygonal mesh simulating geometry of thethree-dimensional object.

The calibrated images may be un-distorted (block 230) based on the lensand camera sensor configurations. The undistorting procedure may removethe radial distortion (which may make straight lines appear as curvedones) and tangential distortion (which may make some areas of the imageto appear closer than expected) due to the image taking lens not beingtruly parallel to the imaging plane).

The undistorted images may then be projected onto the reconstructedpolygonal mesh (block 235) and mapped into its UV layout in order toproduce UV maps. As schematically illustrated by FIG. 21, each projectedimage at least partially covers the UV map, since the field of view ofthe camera that has produced the image covers at least part of thethree-dimensional object of interest (e.g., model's head). In order toproduce a single UV map for each illumination pattern, partial UV mapsproduced by projecting the images acquired by different cameras may beblended by the mask generation (block 240) and fusion blend (block 245)operations. The mask generation produces, for each camera andillumination pattern, a mask that defines a region of the UV map that isbest covered by the field of view of that camera, as described in moredetail herein below. The masked images are then blended by the fusionblend operation, which creates seamless maps without losing the detailsand blurring the image.

FIG. 22 shows the albedo map that has been acquired using the blendingLc (full-on illumination with cross filters) pattern. Each textureelement (“texel”) of the albedo map represents a color value of theobject surface under diffused light with no shadows or light raysreflected by other surfaces. FIG. 23 shows an example blended imagesthat have been acquired using L_(p) (full-on illumination with parallelfilters) pattern. FIG. 24 shows an example blended images that have beenacquired using L_(x) illumination and polarization pattern. FIG. 25shows an example blended images that have been acquired usingL{circumflex over ( )}_(x) illumination and polarization pattern. FIG.26 shows an example blended images that have been acquired using L_(y)illumination and polarization pattern. FIG. 27 shows an example blendedimages that have been acquired using L{circumflex over ( )}_(y)illumination and polarization pattern. FIG. 28 shows an example blendedimages that have been acquired using L_(z) illumination and polarizationpattern. FIG. 29 shows an example blended images that have been acquiredusing L_(z) illumination and polarization pattern.

After blending, the images that have been acquired using L_(p) (full-onillumination with parallel filters) and L_(c) (full-on illumination withcross-polarized filters) patterns, are fed to the reflectancecomputation operation (block 255) which generates the reflectance map260 illustrating distribution of specular reflectance on the surface ofthe three-dimensional object (e.g., model's head), as schematicallyillustrated by FIG. 20. Some regions of a human face may be morereflective than others (e.g., because of the type of skin cells and thecharacteristics of the underlying tissues). The specular reflection isvisible in the images produces using parallel polarization filters, andis filtered by cross-polarized filters. Hence, the reflectance map maybe produced by computing the difference between the images producedusing L_(p) (parallel polarization) and L_(c) (cross-polarization)patterns. The reflectance coefficient for each pixel may be representedby the maximum color value (e.g., in the blue channel) of the pixel.

After blending, images that have been acquired using L_(x), L{circumflexover ( )}_(x), L_(y), L{circumflex over ( )}_(y), L_(z), andL{circumflex over ( )}_(z) parallel-polarized filters and partialillumination patterns, are utilized for generating (block 165) thephotometric normal map 270. FIG. 21 shows an example photometric normalmap. The normal map comprises a plurality of elements, such that eachelement represents a surface normal at a corresponding image point.Normal map generation is based on the theory of spherical harmonic. Thelighting patterns utilized for acquiring the images are designed in sucha manner that the difference between images acquired using L andL{circumflex over ( )} patterns provides information about the surfacenormal direction. In other words, the difference between L_(x) andL{circumflex over ( )}_(x) is used for calculating x component of thesurface normal map (n_(x)), the difference between L_(y) andL{circumflex over ( )}_(y) is used for calculating y component thesurface normal map (n_(y)), and the difference between L_(z) andL{circumflex over ( )}z is used for calculating z component of thesurface normal map (n_(z)). In certain implementations, the differencesmay only be computed for a specific color channel (e.g., the greenchannel):

G _(x) =L _(x) −L{circumflex over ( )} _(x)

G _(y) =L _(y) −L{circumflex over ( )} _(y)

G _(z) =L _(z) −L{circumflex over ( )} _(z)

The computed differences may then be normalized by the value of r=∥G_(x)²+G_(y) ²+G_(z) ²∥ for each pixel to produce the surface normal values:

n _(x) =G _(x) /r

n _(y) =G _(y) /r

n _(z) =G _(z) /r

While the green channel is used in the above computations, the sametechnique can be applied to the red and blue channels. However, the redchannel is not preferred because low frequency light rays may enter thehuman skin and reflect after sub-surface scattering. Consequently, thecorresponding surface normal map is not accurate enough fordemonstrating micro-structures of human skin. Moreover, between thegreen and blue channels (which both capture high frequency light rays),the green channel is preferred because based on Bayer color array, thedigital camera sensor has less blue cells than green cells, and hence,the blue channel is more susceptible to noise.

The final operation (275) of the image processing workflow is generatingthe displacement map 280. Based on the photometric normal map 270, thedisplacement map 280 is generated for the reconstructed polygonal meshproduced by the geometry reconstruction operation 225, such that afterapplying the displacement, the polygonal mesh would encompass alldetails of the surface normal map.

The polygonal meshes and texture maps produced by the image processingworkflow 200 can be utilized in various applications, such asinteractive video games. In an illustrative example, one or more filescontaining the polygonal meshes and textures may be distributed on thecomputer-readable media carrying executable interactive video gamefiles. In another illustrative example, one or more files containing thepolygonal meshes and textures may be downloaded by a client gamingdevice over a network from a gaming server.

Implementations aspects related to several of the above-referencedoperations of the image processing workflow are described in more detailherein below.

As noted herein above, the acquired images are calibrated using areference color chart. “Color calibration” refers to the process ofdetermining and/or the color response of an image acquiring device(e.g., a camera). Color calibration may involve identifying a functionlinking the pixel intensities to a physical property (e.g., the sceneirradiance). Color calibration results affect all other operations ofthe image processing workflow, and thus color calibration may be viewedas one of the most important operations of the workflow.

FIG. 22 schematically illustrates an example image capture sequence. Thelight rays incident onto the lens 221 pass through the lens thus formingthe image plane irradiance 222, which may differ from the sceneirradiance 223 due to various optical phenomena, such as Fernel effectand vignette. The camera sensor 224 measures the image plane irradianceand concerts it to pixel intensities based on its response function. Theresponse function, which depends on the sensor technology andsensitivity, is usually represented by a non-linear function, therefore,the captured image 225 does not uniformly reflect the image planeirradiance 222. Furthermore, the pixel brightness may be affected by theexposure time.

FIG. 23 depicts a flowchart of an example method 2300 for colorcalibration, in accordance with one or more aspects of the presentdisclosure. The example method 2300 and/or each of its individualfunctions, routines, subroutines, or operations may be performed by oneor more processors of a computer system (e.g., the computer system 2900of FIG. 29), and may further employ one or more cameras, lightingassemblies, light synchronization controllers, and/or other equipment.The method 2300 may be performed by one or more processing threads, eachthread executing one or more individual functions, routines,subroutines, or operations of the method.

The target color chart values (block 2310), which may be provided by thecamera sensor manufacturer, may be converted to XYZ and RGB color spaces(blocks 2315-2320), e.g., using D50 white point and Bradford chromaticadaptation, thus producing the target values T and T* in XYZ and RGBcolor spaces, respectively. T* target values may be used for gain andwhite balance adjustment (block 2345), while T target values may be usedfor the main color calibration (block 2350) and tone re-mapping (block2355).

The color calibration workflow may begin (block 2330) by acquiring oneor more images of a color chart, such as Mac Beth color chart shown inFIG. 24, using the same illumination pattern and camera configuration aswill be used for acquiring the input images (block 2335) to be processedby the image processing workflow 200.

After de-noising the acquired color chart image (block 2340),coordinates of the color chart within the image are determined (block2345).

The average color value of each color chip is then determined (block2350), after removing the outlier pixels. Pixels of the computed averagevalues are stored in an image S, which has a pre-defined size, such as4×6. The input image and the sampled chart are normalized by subtractingthe RGB value of the black chip of the color chart from the color ofeach pixel and then dividing the result of the subtraction operation bythe difference between the RGB values of the black chip and the whitechip of the color chart:

$I_{n}^{(i)} = \frac{I^{(i)} - b_{s}}{w_{s} - b_{s}}$$S_{n}^{(i)} = \frac{S^{(i)} - b_{s}}{w_{s} - b_{s}}$

where I denotes the input image to be calibrated,

S is the image comprising the computed average color values of thesample chart,

i is the pixel index,

b_(s) is the RGB value of the black chip of the color chart, and

w_(s) is the RGB value of the white chip of the color chart.

After the normalization, the RGB value of white would be 1 for allchannels regardless of the capture and illumination configurations.

Upon completing the normalization, the optimal gain value may becomputed (block 2355), which is a scalar multiplier that minimizes thesum of per-pixel differences between the normalized color chart and thetarget value in the RGB color space reduced by the RGB value of theblack chip in the target chart:

$\alpha = {\min\limits_{\overset{\sim}{\alpha}}{\sum\limits_{i}\; \left( {{\overset{\_}{T}}^{(i)} - b_{\overset{\_}{T}} - {\overset{\sim}{\alpha}S_{n}^{(i)}}} \right)^{2}}}$

where α denotes the optimal gain value,

T* is the target color chart value for RGB color space (produced atblock 2340) and

b_(T) is the RGB value of the black chip in the target chart.

Using the computed gain value, the adjusted input image and sourcesample values may be calculated as follows:

I _(a) ^((i)) =αl _(n) ^((i)) +b _(T)

S _(a) ^((i)) =αS _(n) ^((i)+b) _(T)

where I_(a) and S_(a) denote the adjusted input image and sourcesamples, respectively.

Then, the best color transformation to XYZ color space may be calculated(block 2360) using the computed adjusted input image and source samplevalues. The color transformation may be represented by a 3×3 matrixwhich is multiplied by the RGB component of each pixel:

$\begin{bmatrix}X \\Y \\Z\end{bmatrix} = {M\begin{bmatrix}R \\G \\B\end{bmatrix}}$

where M denotes the color transformation matrix.

The optimal color transformation matrix is calculated by minimizing thedifference between the target values in the RGB color space and adjustedsource samples:

$M = {\min\limits_{\hat{M}}{\sum\limits_{i}\left( {T^{(i)} - {\hat{M}S_{a}^{(i)}}} \right)^{2}}}$

where T denotes the target values in XYZ color spaces, and

S_(a) denotes the adjusted source sample values.

The computed color transformation may be applied to the adjusted inputimage and source samples to produce the respective XYZ color spacevalues, which may be fed to the tone remapping operation (block 2365):

I _(x) ^((i)) =MI _(a) ^((i))

S _(x) ^((i)) =MS _(a) ^((i))

Tone remapping operation (block 2365) determines three tone curves forX, Y, and Z channels, respectively. For a given color channel intensityC, the tone curve F(C) may be defined as follows:

F(C)=a ₀ C ^(γ) ⁰ +a ₁ C ^(γ) ¹ +a ₂ C ^(γ) ²

Values of parameters α and γ may be determined by minimizing the errorfunction represented by a sum of squared differences between the tonecurve values and XYZ space target values, as follows:

${\sum\limits_{i}\left( {{F\left( {{}_{}^{}{}_{}^{(i)}} \right)} - {{}_{}^{}{}_{}^{(i)}}} \right)^{2}} + {\sum\limits_{i}\left( {{F\left( {{}_{}^{}{}_{}^{(i)}} \right)} - {{}_{}^{}{}_{}^{(i)}}} \right)^{2}} + {\sum\limits_{i}\left( {{F\left( {{}_{}^{}{}_{}^{(i)}} \right)} - {{}_{}^{}{}_{}^{(i)}}} \right)^{2}}$

where ^(X)T, ^(Y)T, and ^(Z)T denote X, Y, Z channels of target values Tin XYZ color space, and

^(X)T_(x), ^(Y)T_(x), and ^(Z)T_(x) denote X, Y, Z channels of adjustedsource samples Sin XYZ color space.

As the error function is non-linear, the optimal parameter values may befound by applying a non-linear optimization technique (such as simulatedannealing or a similar method).

The tone remapping curve F is applied to the pixels of the adjustedinput image I_(x) and produces corresponding pixels of the tone-remappedimage I_(o). The tone remapping operation is optional and may be omittedif the user prefers to use the original tone (i.e., I_(x)=I₀).

The tone-remapped image I_(o) is fed to the operation of conversion toRGB color space (block 2370), which converts I_(o) to RGB color spacebased on Bradford chromatic adaptation and the desired white point(e.g., D65), thus producing the calibrated image (block 2375).

Another aspect of the present disclosure is related to the maskgeneration operation (block 240 of FIG. 2). As noted herein above, inorder to produce a single UV map for each illumination pattern, partialUV maps produced by projecting the images acquired by different camerasmay be blended by the mask generation (block 240 of FIG. 2) and fusionblend (block 245 of FIG. 2) operations. The mask generation operationproduces, for each camera and illumination pattern, a mask that definesthe region of the UV map that is best covered by the field of view ofthat camera. The mask generation operation involves two principalprocedures: computing distortion maps for each camera and generatingmasks. The mask generation technique described herein considersdifferent factors including the perspective stretch, geometrydeformation, the image brightness, and the size of mask islands.

A distortion map has the form of the mesh UV layout; intensity of eachpixel of the distortion map represents a level of distortion that thecamera introduces for that certain pixel. In an illustrative example,for a mesh face which is directly facing the camera (i.e., if the meshsurface normal of the center point of the mesh matches the optical axisof the camera lens), the level of distortion would be close to zero;that level would increase as the angle between the mesh surface normaland the optical axis of the camera lens increases.

Producing the distortion map involves computing depth and position mapsusing the ray tracing technique, as schematically illustrated by FIG.25. For each texture element (“texel”), the distance from the camera tothe ray hit point is stored in the depth map and the position of the rayhit point is stored in the position map. A relative distortion of atexel may be defined as follows:

r _(i,j)=((D _(i−1,j) −D _(i+1,j))²+(P _(i−1,j) −P _(i+1,j))²)*h+((D_(i,j+1) −D _(i,j−1))²+(P _(i,j+1) −P _(i,j−1))²)*h

where r_(i,j) denotes the relative distortion of the texel with imagecoordinates (i,j),

D_(i, j) is the depth of the texel with image coordinates (i,j),

P_(i,j) is the position of the texel with image coordinates (i,j), and

h and w is the height and width of the rendered image, respectively.

Thus, the relative distortion will be zero if the depth and positionremain unchanged for the texel's neighbors. Furthermore, the relativedistortion depends on the resolution of the rendered image. Thus, thetotal distortion of a camera may be computed by summing relativedistortions of several different image resolutions:

R=r ^((w)) +r ^((w/2)) +r ^((w/4)) +r ^((w/8)) +r ^((w/16))

where R denotes the total distortion of a camera (for a given texel),and

r^((w)) is the relative distortion of the image with the image width w,which for the example image processing workflow 200 of FIG. 2 is equalto the width of captured images (i.e., camera resolution).

After computing R (the total distortion of the camera), it is projectedonto the mesh and mapped to the mesh UV in order to create thedistortion map. The distortion map may be utilized for identifying, foreach camera, UV map regions with high distortion (e.g., distortion levelexceeding a pre-defined threshold), as schematically illustrated by FIG.26. The identified high distortion regions may be filtered out(discarded).

Since the number of cameras producing the images for the example imageprocessing workflow 200 of FIG. 2 is relatively high, more than onecamera with an acceptable distortion level may be identified for anygiven UV map region. Thus, an additional factor may be needed for maskgeneration. Such a factor may be represented by the image brightness.Depending on the lighting pattern and camera position and orientation,an image region may appear brighter in one camera in comparison to othercameras, especially if the parallel polarization configuration (e.g.,L_(x)) is used for acquiring the image. Hence, a camera producing thebrightest image may be preferred among several cameras producing imageswith acceptable distortion levels.

Thus, a parallel polarization image and a corresponding distortion mapmay be used for computing a combined distortion and image map asfollows:

E_(l)(i, j) = I_(l)(i, j) if R_(l)(i, j) < D_(T) E_(l)(i, j) = I_(l)(i,j)/(1 + R_(l)(i, j)) if R_(l)(i, j) >= D_(T)

Where l denotes the index of the camera,

E_(l) is the combined distortion and image map for camera l,

I_(l) is a parallel polarization image acquired by camera l,

R_(l) is the distortion map of camera l, and

D_(T) is the distortion threshold.

Using this definition allows only accepting distorted images if there isno other camera to cover the distorted regions.

The cameras may be sorted based on the combined distortion and image mapE values for each pixel, such that the camera with highest E value wouldhave the highest rank. Using strict ordering, only one camera wouldreceive the highest rank for each texel in the UV map.

Then, a camera rank map Si may be produced such that the value of S_(l)(i,j) reflects the rank of camera l (e.g., represented by the index ofthe camera in the sorted list of cameras based on the combineddistortion and image map E values for each pixel). A group of connectedpixels having the same S_(l) value may form an island in the UV map.Each camera rank map S_(l) may be searched for rank 1 islands having thesize (i.e., the number of pixels) exceeding a pre-defined thresholdvalue. The identified rank 1 islands may be exported to the mask ofcamera l, which is denoted as M_(l). The produced masks would notoverlap (as strict ordering has been used for camera ranking). However,the masks would probably not cover the entire UV map.

In order to provide the complete UV map coverage, pixels may graduallybe added to the islands. The neighboring pixels of initial islands ofall cameras may be inserted into a queue that keeps pixels sorted basedon their ranks. In other words, the queue stores tuples of (i, j, r, l),where i, j are the pixel coordinates, r is the rank, and l is the cameraindex. The tuples may iteratively be retrieved, one at a time, from thequeue, until all UV map pixels are covered. For each retrieved tuple (i,j, r, l), if the pixel (i,j) defined by the tuple is not covered by anycamera, the mask of camera l may be updated to include pixel (i,j) andthe uncovered new neighboring pixel may be inserted into the priorityqueue. Thus, the initial islands of rank 1 would gradually grow until nofurther improvement is possible. FIG. 27 shows an example generatedmask.

As an optional feature, the mask generation procedure may be enhanced tosupport symmetric masks. With this option enabled, the input cameras maybe paired based on the symmetry, and symmetric masks may be generatedfor each pair. In order to achieve this, after computing combineddistortion and image maps E for all cameras, a new set of E maps may becalculated by averaging each pixel from El and the corresponding pixelof the flipped E image of its paired camera. The masks are thengenerated for this new E map set only for the left half of the UV map,which after computation is duplicated to the right half for thecorresponding cameras.

As noted herein above, the masked images are then blended by the fusionblend operation (block 245 of FIG. 2). Blending background andforeground images attempts to stitch the images such that the resultingimage would not exhibit any visible seams. FIG. 28 shows example UVmasks before and after performing the fusion blend operation.

The blending procedure starts by blending all masked images into asingle image and applying a Gaussian blur filter to the blendingoperation result, thus producing a blurred image which forms an initialbackground. Next, the first masked image, as the foreground, is blendedwith the initial background image, thus producing the next backgroundimage. The procedure is repeated for all the input images.

The blending procedure may change the pixel intensities, but shouldpreserve the overall image structure and information conveyed by theimage. As the information conveyed by the image may be described byLaplacian of the image, the blending operation may aim to preserveLaplacian of the image while adjusting pixel intensities.

Laplacian is a 2D isotropic measure of the second spatial derivative ofan image:

L(x,y)=∂² I/∂x ²+∂² I/∂y ²

where I(x,y) are the pixel intensity values.

Laplacian of an image highlights regions of rapid intensity change.

In certain implementations, the blending operation may be based uponPoisson image editing, in which the image is decomposed into itsLaplacian and its boundary. A pair of non-overlapping, except for onestrip of boundary pixels, foreground image F and background image B maybe represented as follows:

B=B _(u) +B _(b)

F=F _(u) +F _(b)

where B_(b) and F_(b) are the boundary pixels, and

B_(u) and F_(u) and background and foreground images without theboundary pixels, respectively.

The image blending operation may start by computing the Laplacian of thebackground image (LB). Then, a new image N may be produced by solvingPoison equation for L_(B) and F_(b) and adding the result to F_(u):

N=P(L _(B) ,F _(b))+F _(u)

where P(L_(B), F_(b)) denotes the solution of Poison equation for L_(B)and F_(b).

Thus, the new image N contains the foreground F, but changes thebackground area such that it respects the boundary of F. The result maystill not be seamless as the foreground in not yet adapted.

Next, another image may be produced by solving Poison equation for N andB_(b) and adding the result to Bu:

M=P(N,B _(b))+B _(u)

The resulting image M is a solid and seamless image. Laplacians of bothbackground and foreground are preserved except for boundary pixels andtheir immediate neighbors.

Another aspect of the present disclosure is related to the displacementmap generation operation (block 275 of FIG. 2). As noted herein above,the displacement map 280 is generated based on the photometric normalmap 270 for the reconstructed polygonal mesh produced by the geometryreconstruction operation 225, such that after applying the displacement,the polygonal mesh would encompass all details of the surface normalmap.

A displacement map is a two-dimensional image in which pixel intensitiesdefine corresponding displacement values. Similarly to surface normaland albedo maps, the displacement map is defined on the UV layout of thepolygonal mesh. To displace a vertex, it is moved in direction of itsnormal with the magnitude specified by the displacement value.Regardless of the mesh resolution, a point of the polygonal meshcorresponding to any texel can be determined using inverse projection.The displacement can be defined as follows:

w _((i,j)) =v _((i,j)) +d _((i,j)) f _((i,j))

where w_((i,j)) denotes the updated vertex,

v_((i,j)) is a point of the input mesh corresponding to the UV pixelhaving the coordinates of (i,j),

d_((i,j)) is the amount of displacement, and

f_((i,j)) is the surface normal vector.

It may be desired to calculate the displacement value for each vertexsuch that after performing the displacement, each surface normal wouldbe equal to the photometric normal value. Such constraint is satisfiedif the photometric normal n is orthogonal to the tangent of the updatedmesh. Using the central difference formula, this constraint may beexpressed as follows:

n _((i,j)) ⊥w _((i,j+1)) −w _((i,j−1))

n _((i,j)) ⊥w _((i+1,j)) −w _((i−1,j))

where ⊥ denotes the orthogonality relationship.

Using the above definition of displacement, the orthogonalityconstraints may be expressed as follows:

n _((i,j)) ⊥v _((i,j+1)) −v _((i,j−1)) +d _((i,j+1)) f _((i,j+1)) −d_((i,j−1)) f _((i,j−1))

n _((i,j)) ⊥v _((i+1,j)) −v _((i−1,j)) +d _((i+1,j)) f _((i+1,j)) −d_((i−1,j)) f _((i−1,j))

Applying the inner product operation to these orthogonality constraintsproduces the following system of linear equations:

−n _((i,j)) v _((i,j+1)) −v _((i,j−1))=d _((i,j+1)) n _((i,j)) f_((i,j+1)) −d _((i,j−1)) n _((i,j)) f _((i,j−1))

−n _((i,j)) v _((i+1,j)) −v _((i−1,j)) =d _((i+1,j)) n _((i,j)) f_((i+1,j)) −d _((i−1,j)) n _((i,j)) f _((i−1,j))

Solving the system of linear equations would produce the optimaldisplacement values d.

In order to apply the displacement map, the computed vertices areinserted to the input mesh. In other words, for each texel of the UVlayout map, a vertex is inserted into a corresponding position of themesh.

FIG. 29 illustrates a diagrammatic representation of a computing device2900 which may implement the systems and methods described herein.Computing device 2900 may be connected to other computing devices in aLAN, an intranet, an extranet, and/or the Internet. The computing devicemay operate in the capacity of a server machine in client-server networkenvironment. The computing device may be provided by a personal computer(PC), a set-top box (STB), a server, a network router, switch or bridge,or any machine capable of executing a set of instructions (sequential orotherwise) that specify actions to be taken by that machine. Further,while only a single computing device is illustrated, the term “computingdevice” shall also be taken to include any collection of computingdevices that individually or jointly execute a set (or multiple sets) ofinstructions to perform the methods discussed herein.

The example computing device 2900 may include a processing device (e.g.,a general purpose processor) 2902, a main memory 2904 (e.g., synchronousdynamic random access memory (DRAM), read-only memory (ROM)), a staticmemory 2906 (e.g., flash memory and a data storage device 2918), whichmay communicate with each other via a bus 2930.

Processing device 2902 may be provided by one or more general-purposeprocessing devices such as a microprocessor, central processing unit, orthe like. In an illustrative example, processing device 2902 maycomprise a complex instruction set computing (CISC) microprocessor,reduced instruction set computing (RISC) microprocessor, very longinstruction word (VLIW) microprocessor, or a processor implementingother instruction sets or processors implementing a combination ofinstruction sets. Processing device 2902 may also comprise one or morespecial-purpose processing devices such as an application specificintegrated circuit (ASIC), a field programmable gate array (FPGA), adigital signal processor (DSP), network processor, or the like. Theprocessing device 2902 may be configured to execute module 1526implementing workflows 200 and/or 2200 for generating visual objectsrepresenting a person based on two-dimensional images of at least a partof the person's body, in accordance with one or more aspects of thepresent disclosure, for performing the operations and steps discussedherein.

Computing device 2900 may further include a network interface device2908 which may communicate with a network 2920. The computing device2900 also may include a video display unit 2929 (e.g., a liquid crystaldisplay (LCD) or a cathode ray tube (CRT)), an alphanumeric input device2912 (e.g., a keyboard), a cursor control device 2914 (e.g., a mouse)and an acoustic signal generation device 2916 (e.g., a speaker). In oneembodiment, video display unit 2929, alphanumeric input device 2912, andcursor control device 2914 may be combined into a single component ordevice (e.g., an LCD touch screen).

Data storage device 2918 may include a computer-readable storage medium2928 on which may be stored one or more sets of instructions, e.g.,instructions of module 2926 implementing workflows 200 and/or 2200 forgenerating three-dimensional visual objects representing a person basedon two-dimensional images of at least a part of the person's body.Instructions implementing module 2926 may also reside, completely or atleast partially, within main memory 2904 and/or within processing device2902 during execution thereof by computing device 2900, main memory 2904and processing device 2902 also constituting computer-readable media.The instructions may further be transmitted or received over a network2920 via network interface device 2908.

While computer-readable storage medium 2928 is shown in an illustrativeexample to be a single medium, the term “computer-readable storagemedium” should be taken to include a single medium or multiple media(e.g., a centralized or distributed database and/or associated cachesand servers) that store the one or more sets of instructions. The term“computer-readable storage medium” shall also be taken to include anymedium that is capable of storing, encoding or carrying a set ofinstructions for execution by the machine and that cause the machine toperform the methods described herein. The term “computer-readablestorage medium” shall accordingly be taken to include, but not belimited to, solid-state memories, optical media and magnetic media.

Unless specifically stated otherwise, terms such as “updating”,“identifying”, “determining”, “sending”, “assigning”, or the like, referto actions and processes performed or implemented by computing devicesthat manipulates and transforms data represented as physical(electronic) quantities within the computing device's registers andmemories into other data similarly represented as physical quantitieswithin the computing device memories or registers or other suchinformation storage, transmission or display devices. Also, the terms“first,” “second,” “third,” “fourth,” etc. as used herein are meant aslabels to distinguish among different elements and may not necessarilyhave an ordinal meaning according to their numerical designation.

Examples described herein also relate to an apparatus for performing themethods described herein. This apparatus may be specially constructedfor the required purposes, or it may comprise a general purposecomputing device selectively programmed by a computer program stored inthe computing device. Such a computer program may be stored in acomputer-readable non-transitory storage medium.

The methods and illustrative examples described herein are notinherently related to any particular computer or other apparatus.Various general purpose systems may be used in accordance with theteachings described herein, or it may prove convenient to construct morespecialized apparatus to perform the required method steps. The requiredstructure for a variety of these systems will appear as set forth in thedescription above.

The above description is intended to be illustrative, and notrestrictive. Although the present disclosure has been described withreferences to specific illustrative examples, it will be recognized thatthe present disclosure is not limited to the examples described. Thescope of the disclosure should be determined with reference to thefollowing claims, along with the full scope of equivalents to which theclaims are entitled.

What is claimed is:
 1. A method, comprising: receiving a plurality ofinput images of a three-dimensional object, wherein the plurality ofinput images has been acquired by a plurality of cameras using aplurality of illumination and polarization patterns; producing aplurality of adjusted images by adjusting each input image of theplurality of input images by a pre-computed gain value; producing aplurality of color-calibrated images by applying a color transformationmatrix to each adjusted image of the plurality of adjusted images;generating, using the plurality of color-calibrated images, a polygonalmesh simulating geometry of the three-dimensional object; producing aplurality of partial UV maps by projecting the plurality ofcolor-calibrated images onto the polygonal mesh; generating a pluralityof masks, wherein each mask of the plurality of masks is associated witha camera of the plurality of cameras, wherein the mask defines a UVspace region that is covered by a field of view of the camera; blending,using the plurality of masks, the plurality of partial UV maps; andgenerating one or more texture maps representing the three-dimensionalobject.
 2. The method of claim 1, further comprising: utilizing thetexture maps for creating an interactive video game.
 3. The method ofclaim 1, wherein the pre-computed gain values minimizes a sum ofper-pixel differences between a normalized color chart and target valuesof RGB color space adjusted by an RGB value of a black color chip in thenormalized color chart.
 4. The method of claim 1, further comprising:computing the color transformation matrix by minimizing a differencebetween target values in an RGB color space and components of a samplecolor chart.
 5. The method of claim 1, further comprising: applying anon-linear tone remapping operation to the plurality of color-calibratedimages, wherein a parameter of the non-linear tone remapping operationis computed by minimizing an error function represented by a sum ofdifferences between tone curve values and adjusted color sample values.6. The method of claim 1, wherein the texture maps comprise areflectance map illustrating distribution of specular reflectance on asurface of the three-dimensional object.
 7. The method of claim 1,wherein the texture maps comprise an albedo map produced from aplurality of images acquired under full-on illumination withcross-polarized filters.
 8. The method of claim 1, wherein the texturemaps comprise a normal map produced from a plurality of images acquiredunder partial illumination with parallel-polarized filters.
 9. Themethod of claim 1, wherein the texture maps comprise a displacement mapcomprising a plurality of elements, each element defining a displacementvalues for a corresponding texture element.
 10. The method of claim 1,further comprising: determining positions of a plurality of markers of apre-defined color within each input image of the plurality of inputimages.
 11. The method of claim 1, wherein blending the plurality ofpartial UV maps further comprises: producing a background image byblending a plurality of masked images; blending a next masked image ofthe plurality of masked images with the background image to produce anew background image; iteratively repeating the blending operation forall masked image of the plurality of masked images.
 12. A method,comprising: receiving a plurality of input images of a three-dimensionalobject, wherein the plurality of input images has been acquired by aplurality of cameras; performing color calibration of the plurality ofinput images to produce a plurality of color-calibrated images;undistorting the plurality of color-calibrated images to remove at leastone of: radial distortion or tangential distortion; producing aplurality of partial UV maps by projecting the plurality ofcolor-calibrated images onto a polygonal mesh simulating geometry of thethree-dimensional object; generating a plurality of masks, wherein eachmask of the plurality of masks is associated with a camera of theplurality of cameras, wherein the mask defines a UV space region that iscovered by a field of view of the camera; producing an initialbackground by blending, using the plurality of masks, the plurality ofpartial UV maps; producing a blended image by iteratively blending eachpartial UV map of the plurality of partial UV maps with the initialbackground; and generating, based on the blended image, one or moretexture maps representing the three-dimensional object.
 13. The methodof claim 12, further comprising: utilizing the texture maps for creatingan interactive video game.
 14. The method of claim 12, wherein thetexture maps comprise at least one of: a reflectance map, an albedo map,a normal map, or a displacement map.
 15. The method of claim 12, furthercomprising: determining, using the plurality of input images, positionsof a plurality of markers of a pre-defined color within each input imageof the plurality of input images; and generating, using the positions ofthe plurality of markers, the polygonal mesh.
 16. The method of claim12, further comprising: applying a non-linear tone remapping operationto the plurality of color-calibrated images, wherein a parameter of thenon-linear tone remapping operation is computed by minimizing an errorfunction represented by a sum of differences between tone curve valuesand adjusted color sample values.
 17. The method of claim 12, whereingenerating the plurality of masks further comprises: generating aplurality of combined distortion and image maps, wherein each combineddistortion and image map comprises a plurality of pixels, whereinintensity of each pixel represents one of: producing, for each camera, acamera rank map based on the combined distortion and image maps; andproducing, using the camera rank map, a mask for the camera.
 18. Acomputer-readable non-transitory storage medium comprising executableinstructions that, when executed by a processor, cause the processor to:receive a plurality of input images of a three-dimensional object,wherein the plurality of input images has been acquired by a pluralityof cameras using a plurality of illumination and polarization patterns;produce a plurality of adjusted images by adjusting each input image ofthe plurality of input images by a pre-computed gain value; produce aplurality of color-calibrated images by applying a color transformationmatrix to each adjusted image of the plurality of adjusted images;generate, using the plurality of color-calibrated images, a polygonalmesh simulating geometry of the three-dimensional object; produce aplurality of partial UV maps by projecting the plurality ofcolor-calibrated images onto the polygonal mesh; generate a plurality ofmasks, wherein each mask of the plurality of masks is associated with acamera of the plurality of cameras, wherein the mask defines a UV spaceregion that is covered by a field of view of the camera; blend, usingthe plurality of masks, the plurality of partial UV maps; and generateone or more texture maps representing the three-dimensional object. 19.The computer-readable non-transitory storage medium of claim 18, furthercomprising executable instructions that, when executed by the processor,cause the processor to: utilize the texture maps for creating aninteractive video game.
 20. The computer-readable non-transitory storagemedium of claim 18, further comprising executable instructions that,when executed by the processor, cause the processor to: apply anon-linear tone remapping operation to the plurality of color-calibratedimages, wherein a parameter of the non-linear tone remapping operationis computed by minimizing an error function represented by a sum ofdifferences between tone curve values and adjusted color sample values.